{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This (article] [http://pbpython.com/simple-graphing-pandas.html] will walk through how to start doing some simple graphing in pandas.\n",
    "I am using a new data file that is the same format as my previous article but includes data for only 20 customers.\n",
    "First we are going to import pandas, numpy and matplot lib. \n",
    "I am also showing the versions I'm testing so you can make sure yours is compatible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.18.1'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "pd.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, enable iPython to display matplotlib graphs. As an alternative you can run ipython notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will read in the file like we did in the previous article but I'm going to tell it to treat the date column as a date field so I can do some re-sampling later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account number</th>\n",
       "      <th>name</th>\n",
       "      <th>sku</th>\n",
       "      <th>category</th>\n",
       "      <th>quantity</th>\n",
       "      <th>unit price</th>\n",
       "      <th>ext price</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>199372</td>\n",
       "      <td>Cruickshank and Sons</td>\n",
       "      <td>FQ-39509</td>\n",
       "      <td>Belt</td>\n",
       "      <td>12</td>\n",
       "      <td>65.26</td>\n",
       "      <td>783.12</td>\n",
       "      <td>2014-12-06 15:02:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>518580</td>\n",
       "      <td>Hessel-Lind</td>\n",
       "      <td>XV-77832</td>\n",
       "      <td>Shirt</td>\n",
       "      <td>14</td>\n",
       "      <td>28.26</td>\n",
       "      <td>395.64</td>\n",
       "      <td>2014-10-29 14:54:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>368293</td>\n",
       "      <td>Hodkiewicz-Kozey</td>\n",
       "      <td>BC-56182</td>\n",
       "      <td>Belt</td>\n",
       "      <td>4</td>\n",
       "      <td>54.93</td>\n",
       "      <td>219.72</td>\n",
       "      <td>2015-03-11 21:45:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>677566</td>\n",
       "      <td>Boyle-Wiegand</td>\n",
       "      <td>BH-22979</td>\n",
       "      <td>Belt</td>\n",
       "      <td>4</td>\n",
       "      <td>65.37</td>\n",
       "      <td>261.48</td>\n",
       "      <td>2014-05-21 16:07:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>677566</td>\n",
       "      <td>Boyle-Wiegand</td>\n",
       "      <td>FQ-39509</td>\n",
       "      <td>Belt</td>\n",
       "      <td>19</td>\n",
       "      <td>35.34</td>\n",
       "      <td>671.46</td>\n",
       "      <td>2015-04-15 12:50:05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   account number                  name       sku category  quantity  \\\n",
       "0          199372  Cruickshank and Sons  FQ-39509     Belt        12   \n",
       "1          518580           Hessel-Lind  XV-77832    Shirt        14   \n",
       "2          368293      Hodkiewicz-Kozey  BC-56182     Belt         4   \n",
       "3          677566         Boyle-Wiegand  BH-22979     Belt         4   \n",
       "4          677566         Boyle-Wiegand  FQ-39509     Belt        19   \n",
       "\n",
       "   unit price  ext price                date  \n",
       "0       65.26     783.12 2014-12-06 15:02:14  \n",
       "1       28.26     395.64 2014-10-29 14:54:18  \n",
       "2       54.93     219.72 2015-03-11 21:45:30  \n",
       "3       65.37     261.48 2014-05-21 16:07:44  \n",
       "4       35.34     671.46 2015-04-15 12:50:05  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales=pd.read_csv(\"sample-salesv2.csv\",parse_dates=['date'])\n",
    "sales.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have read in the data, we can do some quick analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account number</th>\n",
       "      <th>quantity</th>\n",
       "      <th>unit price</th>\n",
       "      <th>ext price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>496998.477000</td>\n",
       "      <td>10.450000</td>\n",
       "      <td>55.465770</td>\n",
       "      <td>580.21763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>271588.313857</td>\n",
       "      <td>5.777272</td>\n",
       "      <td>25.621962</td>\n",
       "      <td>445.17188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>62767.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.350000</td>\n",
       "      <td>12.85000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>200626.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>33.900000</td>\n",
       "      <td>213.72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>518580.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>56.400000</td>\n",
       "      <td>462.37500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>687215.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>77.610000</td>\n",
       "      <td>872.76750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>968503.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>99.580000</td>\n",
       "      <td>1906.60000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       account number     quantity   unit price   ext price\n",
       "count     1000.000000  1000.000000  1000.000000  1000.00000\n",
       "mean    496998.477000    10.450000    55.465770   580.21763\n",
       "std     271588.313857     5.777272    25.621962   445.17188\n",
       "min      62767.000000     1.000000    10.350000    12.85000\n",
       "25%     200626.000000     5.000000    33.900000   213.72000\n",
       "50%     518580.000000    11.000000    56.400000   462.37500\n",
       "75%     687215.000000    15.000000    77.610000   872.76750\n",
       "max     968503.000000    20.000000    99.580000  1906.60000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can actually learn some pretty helpful info from this simple command:\n",
    "For example, we can tell that customers on average purchases 10.3 items per transaction and that the average cost of the transaction was $579.84. It is also easy to see the min and max so you understand the range of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1000.000000\n",
       "mean       55.465770\n",
       "std        25.621962\n",
       "min        10.350000\n",
       "25%        33.900000\n",
       "50%        56.400000\n",
       "75%        77.610000\n",
       "max        99.580000\n",
       "Name: unit price, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales['unit price'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is easy to call describe on a single column too. I can see that my average price is \\$56.18 but it ranges from \\$10.06 to \\$99.97."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am showing the output of dtypes so that you can see that the date column is a datetime field. I also scan this to make sure that any columns that have numbers are floats or ints so that I can do additional analysis in the future."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "account number             int64\n",
       "name                      object\n",
       "sku                       object\n",
       "category                  object\n",
       "quantity                   int64\n",
       "unit price               float64\n",
       "ext price                float64\n",
       "date              datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we remove some columns to make additional analysis easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>ext price</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cruickshank and Sons</td>\n",
       "      <td>783.12</td>\n",
       "      <td>2014-12-06 15:02:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hessel-Lind</td>\n",
       "      <td>395.64</td>\n",
       "      <td>2014-10-29 14:54:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hodkiewicz-Kozey</td>\n",
       "      <td>219.72</td>\n",
       "      <td>2015-03-11 21:45:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Boyle-Wiegand</td>\n",
       "      <td>261.48</td>\n",
       "      <td>2014-05-21 16:07:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Boyle-Wiegand</td>\n",
       "      <td>671.46</td>\n",
       "      <td>2015-04-15 12:50:05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   name  ext price                date\n",
       "0  Cruickshank and Sons     783.12 2014-12-06 15:02:14\n",
       "1           Hessel-Lind     395.64 2014-10-29 14:54:18\n",
       "2      Hodkiewicz-Kozey     219.72 2015-03-11 21:45:30\n",
       "3         Boyle-Wiegand     261.48 2014-05-21 16:07:44\n",
       "4         Boyle-Wiegand     671.46 2015-04-15 12:50:05"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers = sales[['name','ext price','date']]\n",
    "customers.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This representation has multiple lines for each customer. In order to understand purchasing patterns, let's group all the customers by name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name\n",
       "Boyer-Kerluke                         50\n",
       "Boyle-Wiegand                         53\n",
       "Braun-Wehner                          65\n",
       "Crona-Leannon                         51\n",
       "Cruickshank and Sons                  46\n",
       "Feest LLC                             54\n",
       "Heller Inc                            38\n",
       "Hessel-Lind                           57\n",
       "Hills Ltd                             52\n",
       "Hodkiewicz-Kozey                      36\n",
       "Johnston-Deckow                       54\n",
       "Kautzer, Kunze and Eichmann           48\n",
       "Keebler PLC                           51\n",
       "Kiehn, Metz and Turcotte              49\n",
       "Leuschke Inc                          55\n",
       "Little and Sons                       57\n",
       "Mitchell PLC                          41\n",
       "Moore, Stehr and Carroll              56\n",
       "Nolan, Koepp and McDermott            49\n",
       "Schulist, Christiansen and Douglas    38\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_group = customers.groupby('name')\n",
    "customer_group.size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that our data is in a simple format to manipulate, let's determine how much each customer purchased during our time frame.\n",
    "\n",
    "The sum function allows us to quickly sum up all the values by customer. We can also sort the data using the sort command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ext price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mitchell PLC</th>\n",
       "      <td>18855.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hodkiewicz-Kozey</th>\n",
       "      <td>19900.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schulist, Christiansen and Douglas</th>\n",
       "      <td>22122.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cruickshank and Sons</th>\n",
       "      <td>22716.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heller Inc</th>\n",
       "      <td>23224.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    ext price\n",
       "name                                         \n",
       "Mitchell PLC                         18855.52\n",
       "Hodkiewicz-Kozey                     19900.27\n",
       "Schulist, Christiansen and Douglas   22122.12\n",
       "Cruickshank and Sons                 22716.09\n",
       "Heller Inc                           23224.74"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_totals = customer_group.sum()\n",
    "sales_totals.sort_values(by=['ext price']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we know what the data look like, tt is very simple to create a quick bar chart plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bWwBbSNortx8JPG17c+As4IxOfLkgCIKgMU2VgO15tu/Pz/8OPASMzm/X2067P3Cp7UW2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\nESacQBAEwQjz/5xayVIHhm0uAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc74e2e99e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_plot = sales_totals.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately this chart is a little ugly. With a few tweaks we can make it a little more impactful.\n",
    "Let's try:\n",
    "- sorting the data in descending order.\n",
    "- Removing the legend\n",
    "- Adding a title\n",
    "- Labeling the axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fc74b8725c0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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I90JHuLCCIAiCoDRhQIIgCIJahAEJgiAIahEGJAiCIKhFGJAgCIKgFmFAgiAI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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc74bd8c278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_plot = sales_totals.sort_values(by=['ext price'],ascending=False).plot(kind='bar',legend=None,title=\"Total Sales by Customer\")\n",
    "my_plot.set_xlabel(\"Customers\")\n",
    "my_plot.set_ylabel(\"Sales ($)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This actually tells us a little about our biggest customers and how much difference there is between their sales and our smallest customers.\n",
    "\n",
    "Now, let's try to see how the sales break down by category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>category</th>\n",
       "      <th>ext price</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cruickshank and Sons</td>\n",
       "      <td>Belt</td>\n",
       "      <td>783.12</td>\n",
       "      <td>2014-12-06 15:02:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hessel-Lind</td>\n",
       "      <td>Shirt</td>\n",
       "      <td>395.64</td>\n",
       "      <td>2014-10-29 14:54:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hodkiewicz-Kozey</td>\n",
       "      <td>Belt</td>\n",
       "      <td>219.72</td>\n",
       "      <td>2015-03-11 21:45:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Boyle-Wiegand</td>\n",
       "      <td>Belt</td>\n",
       "      <td>261.48</td>\n",
       "      <td>2014-05-21 16:07:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Boyle-Wiegand</td>\n",
       "      <td>Belt</td>\n",
       "      <td>671.46</td>\n",
       "      <td>2015-04-15 12:50:05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   name category  ext price                date\n",
       "0  Cruickshank and Sons     Belt     783.12 2014-12-06 15:02:14\n",
       "1           Hessel-Lind    Shirt     395.64 2014-10-29 14:54:18\n",
       "2      Hodkiewicz-Kozey     Belt     219.72 2015-03-11 21:45:30\n",
       "3         Boyle-Wiegand     Belt     261.48 2014-05-21 16:07:44\n",
       "4         Boyle-Wiegand     Belt     671.46 2015-04-15 12:50:05"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers = sales[['name','category','ext price','date']]\n",
    "customers.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use groupby to organize the data by category and name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>ext price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th>category</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Boyer-Kerluke</th>\n",
       "      <th>Belt</th>\n",
       "      <td>13201.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shirt</th>\n",
       "      <td>19043.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shoes</th>\n",
       "      <td>3748.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Boyle-Wiegand</th>\n",
       "      <th>Belt</th>\n",
       "      <td>10959.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shirt</th>\n",
       "      <td>12274.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        ext price\n",
       "name          category           \n",
       "Boyer-Kerluke Belt       13201.21\n",
       "              Shirt      19043.45\n",
       "              Shoes       3748.47\n",
       "Boyle-Wiegand Belt       10959.84\n",
       "              Shirt      12274.01"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "category_group=customers.groupby(['name','category']).sum()\n",
    "category_group.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The category representation looks good but we need to break it apart to graph it as a stacked bar graph. Unstack can do this for us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">ext price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>category</th>\n",
       "      <th>Belt</th>\n",
       "      <th>Shirt</th>\n",
       "      <th>Shoes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Boyer-Kerluke</th>\n",
       "      <td>13201.21</td>\n",
       "      <td>19043.45</td>\n",
       "      <td>3748.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boyle-Wiegand</th>\n",
       "      <td>10959.84</td>\n",
       "      <td>12274.01</td>\n",
       "      <td>4199.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Braun-Wehner</th>\n",
       "      <td>13841.64</td>\n",
       "      <td>15916.63</td>\n",
       "      <td>6861.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Crona-Leannon</th>\n",
       "      <td>12754.64</td>\n",
       "      <td>9863.25</td>\n",
       "      <td>6332.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cruickshank and Sons</th>\n",
       "      <td>10770.14</td>\n",
       "      <td>5914.07</td>\n",
       "      <td>6031.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     ext price                   \n",
       "category                  Belt     Shirt    Shoes\n",
       "name                                             \n",
       "Boyer-Kerluke         13201.21  19043.45  3748.47\n",
       "Boyle-Wiegand         10959.84  12274.01  4199.82\n",
       "Braun-Wehner          13841.64  15916.63  6861.65\n",
       "Crona-Leannon         12754.64   9863.25  6332.65\n",
       "Cruickshank and Sons  10770.14   5914.07  6031.88"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "category_group.unstack().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fc74b763ac8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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706RJk4y24Vx9UeUFiKQ8SR8Ay4AXw0KgjZktBzCzZUDrcPX2QGxF95IwrD2w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\noRX/HxGO/OZbv9X7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc74b7aceb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_plot = category_group.unstack().plot(kind='bar',stacked=True,title=\"Total Sales by Customer\")\n",
    "my_plot.set_xlabel(\"Customers\")\n",
    "my_plot.set_ylabel(\"Sales\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now clean some of this up a little bit.\n",
    "We can specify the figure size and customize the legend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc74b7703c8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MLPKczMzMrGcrNLmJiN8B/dp46I42ylr2ORc4t43yx4Hd2ihfSjZ93MzMzMwr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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc74b6fd908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_plot = category_group.unstack().plot(kind='bar',stacked=True,title=\"Total Sales by Customer\",figsize=(9, 7))\n",
    "my_plot.set_xlabel(\"Customers\")\n",
    "my_plot.set_ylabel(\"Sales\")\n",
    "my_plot.legend([\"Total\",\"Belts\",\"Shirts\",\"Shoes\"], loc=9,ncol=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we know who the biggest customers are and how they purchase products, we might want to look at purchase patterns in more detail.\n",
    "\n",
    "Let's take another look at the data and try to see how large the individual purchases are. A histogram allows us to group purchases together so we can see how big the customer transactions are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ext price</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>783.12</td>\n",
       "      <td>2014-12-06 15:02:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>395.64</td>\n",
       "      <td>2014-10-29 14:54:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>219.72</td>\n",
       "      <td>2015-03-11 21:45:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>261.48</td>\n",
       "      <td>2014-05-21 16:07:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>671.46</td>\n",
       "      <td>2015-04-15 12:50:05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ext price                date\n",
       "0     783.12 2014-12-06 15:02:14\n",
       "1     395.64 2014-10-29 14:54:18\n",
       "2     219.72 2015-03-11 21:45:30\n",
       "3     261.48 2014-05-21 16:07:44\n",
       "4     671.46 2015-04-15 12:50:05"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchase_patterns = sales[['ext price','date']]\n",
    "purchase_patterns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fc74b5c3128>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0um3KFbSZWUXk+TXeMiaQdYGlwH7Aw8AtwFERsaTQwMzMbA2lG8KKiBcl/Suw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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc74b84db70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "purchase_plot = purchase_patterns['ext price'].hist(bins=20)\n",
    "purchase_plot.set_title(\"Purchase Patterns\")\n",
    "purchase_plot.set_xlabel(\"Order Amount($)\")\n",
    "purchase_plot.set_ylabel(\"Number of orders\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After looking at this group\n",
    "\n",
    "We can look at purchase patterns over time. We can see that most of our transactions are less than $500 and only a very few are about $1500."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another interesting way to look at the data would be by sales over time. Do we have certain months where we are busier than others?\n",
    "\n",
    "Let's get the data down to order size and date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ext price</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>783.12</td>\n",
       "      <td>2014-12-06 15:02:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>395.64</td>\n",
       "      <td>2014-10-29 14:54:18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>219.72</td>\n",
       "      <td>2015-03-11 21:45:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>261.48</td>\n",
       "      <td>2014-05-21 16:07:44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>671.46</td>\n",
       "      <td>2015-04-15 12:50:05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ext price                date\n",
       "0     783.12 2014-12-06 15:02:14\n",
       "1     395.64 2014-10-29 14:54:18\n",
       "2     219.72 2015-03-11 21:45:30\n",
       "3     261.48 2014-05-21 16:07:44\n",
       "4     671.46 2015-04-15 12:50:05"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchase_patterns = sales[['ext price','date']]\n",
    "purchase_patterns.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to analyze the data by date, we need to set the date column as the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ext price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-12-06 15:02:14</th>\n",
       "      <td>783.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-10-29 14:54:18</th>\n",
       "      <td>395.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-11 21:45:30</th>\n",
       "      <td>219.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-05-21 16:07:44</th>\n",
       "      <td>261.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-15 12:50:05</th>\n",
       "      <td>671.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     ext price\n",
       "date                          \n",
       "2014-12-06 15:02:14     783.12\n",
       "2014-10-29 14:54:18     395.64\n",
       "2015-03-11 21:45:30     219.72\n",
       "2014-05-21 16:07:44     261.48\n",
       "2015-04-15 12:50:05     671.46"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchase_patterns = purchase_patterns.set_index('date')\n",
    "purchase_patterns.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the really cool things that pandas allows us to do is resample the data. If we want to look at the data by month, we can easily resample and sum it all up."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "purchase_patterns.resample('M',how=sum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the data is now very easy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2qH2z0403hmGNDXU0U313ww3heeG33pr9MUq1qSnhiCNCB3YhlmjxjmvCRJz7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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc74b532eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "purchase_plot = purchase_patterns.resample('M').sum().plot(title=\"Total Sales by Month\",legend=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "December is our peak month and April is the slowest.\n",
    "\n",
    "Let's say we really like this plot and want to save it somewhere for a presentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fig = purchase_plot.get_figure()\n",
    "fig.savefig(\"total-sales.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
